AS-Solar, a Tool for Predictive Maintenance of Solar Groundwater Pumping Systems
Energy for water abstraction limits the viability of some irrigable areas. Increasing efficiency and introducing renewable energy can reduce energy cost. Solar pumping is a widely recognized renewable energy solution. These pumping systems suffer special wear out due to sudden changes and for having...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
MDPI AG
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ed71d2bb3ac742e080208e587c2f8a57 |
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| Sumario: | Energy for water abstraction limits the viability of some irrigable areas. Increasing efficiency and introducing renewable energy can reduce energy cost. Solar pumping is a widely recognized renewable energy solution. These pumping systems suffer special wear out due to sudden changes and for having working conditions far from the nominal points. Thus, monitoring systems are becoming more frequent for maintenance issues. A new decision support system, named AS-Solar, was developed to perform predictive maintenance. This model permits detecting if the source of the anomaly in the pump performance is the pump, the electrical components (motor, variable frequency drive (VFD) or cables) or the pumping pipe. It demands real-time data from the monitoring system and an accurate simulation model, together with an optimization process that helps in the decision making in predictive maintenance. To validate the developed model, it was applied to a complex case study of a solar pumping system of 40 kWp that abstracts groundwater from nearly 200 m deep. This pumping system has a VFD, two lines of cables up to the pump and aggressive water with slimes, which causes different problems in the pumping system. In this case study, the AS-Solar model shows an acceptable accuracy, with a relative error (RE) of the 2.9% in simulated power and 7.9% in simulated discharge. |
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